Using Covid 19 cases of all types data

options & settings

chunk options

CSS for scrollable output & Header colors

Turning scientific / Exponential numbers off

options(scipen = 999)

Loading libs

library(tidyverse)
library(ggthemes)

library(lubridate)
library(covid19.analytics)
library(data.table)
library(plotly)
library(gghighlight)
library(zoo)   # this is for moving averages
# library(ggThemeAssist) 
library(glue)
library(scales)

Creating & setting custom theme

theme_viny_bright <- function(){
  
  library(ggthemes)
  
  ggthemes::theme_fivethirtyeight() %+replace%
  
  theme(
    axis.title = element_text(),
    
    axis.text = element_text(size = 13),
    
    legend.text = element_text(size = 10),
    
    panel.background = element_rect(fill = "white"),
    
    plot.background = element_rect(fill = "white"),
    
    strip.background = element_blank(),
    
    legend.background = element_rect(fill = NA),
    
    legend.key = element_rect(fill = NA),

    plot.title = element_text(hjust = 0.5,
                              size = 19,
                              face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, colour = "maroon")
      )
  
  }

theme_set(theme_viny_bright())

Loading data

Covid19 data

df_all_cases <- covid19.analytics::covid19.data()

df_all_cases %>% head()

df_all_cases <- df_all_cases %>% 
  rename(Incident_Rate = Incidence_Rate,
         Case_Fatality_Ratio = Case.Fatality_Ratio
)

Case_Fatality_Ratio

Deaths * 100 / Confirmed

2067 * 100 / 49817

df_all_cases %>% 
  arrange(desc(Incident_Rate))

dim(df_all_cases)

df_all_cases %>% 
  slice_max(order_by = Case_Fatality_Ratio, n = 15)

gapminder data

library(gapminder)

gapminder %>% head()

Data Prep.

continent_lookup <- gapminder %>% 
  filter(year == max(year)) %>%   
  select(-year)

continent_lookup

df_all_cases <- df_all_cases %>% 
  left_join(continent_lookup, by = c("Country_Region" = "country"))

df_all_cases

df_all_cases <- df_all_cases %>% 
  select(Province_State, Country_Region, continent, pop, gdpPercap, lifeExp, 
         Confirmed, Deaths, Recovered, Active, Incident_Rate, Case_Fatality_Ratio, everything())

df_all_cases

df_all_cases <- df_all_cases %>% 
  mutate(continent = as.character(continent))

df_all_cases

df_all_cases[which(is.na(df_all_cases$continent)), "continent"] <- "Unknown" 

df_all_cases

df_all_cases <- df_all_cases %>% 
  mutate(continent = as.factor(continent))

table(df_all_cases$continent)

df_all_cases %>% 
  filter(continent == "Unknown") %>% 
  select(Country_Region) %>% 
  unique()

Filling continent values

Continent Names

df_all_cases[df_all_cases$Country_Region == "US", "continent"] <- "Americas"
df_all_cases[df_all_cases$Country_Region == "Bhutan", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Brunei", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Burma", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Maldives", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Qatar", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "United Arab Emirates", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Taiwan*", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Monaco", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Luxembourg", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Lithuania", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Russia", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Ukraine", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Tajikistan", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Uzbekistan", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Korea, South", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Czechia", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Andorra", "continent"] <- "Europe"
df_all_cases[df_all_cases$Country_Region == "Antigua and Barbuda", "continent"] <- "Americas"
df_all_cases[df_all_cases$Country_Region == "Armenia", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Yemen", "continent"] <- "Asia"
df_all_cases[df_all_cases$Country_Region == "Cyprus", "continent"] <- "Europe"

Population

df_all_cases[df_all_cases$Country_Region == "US", "pop"] <- 330052960
df_all_cases[df_all_cases$Country_Region == "Bhutan", "pop"] <- 771608
df_all_cases[df_all_cases$Country_Region == "Brunei", "pop"] <- 437479
df_all_cases[df_all_cases$Country_Region == "Burma", "pop"] <- 54409800
df_all_cases[df_all_cases$Country_Region == "Maldives", "pop"] <- 540544
df_all_cases[df_all_cases$Country_Region == "Qatar", "pop"] <- 2881053
df_all_cases[df_all_cases$Country_Region == "United Arab Emirates", "pop"] <- 9890402
df_all_cases[df_all_cases$Country_Region == "Taiwan*", "pop"] <- 23816775
df_all_cases[df_all_cases$Country_Region == "Monaco", "pop"] <- 39242
df_all_cases[df_all_cases$Country_Region == "Luxembourg", "pop"] <- 625978
df_all_cases[df_all_cases$Country_Region == "Lithuania", "pop"] <- 2722289
df_all_cases[df_all_cases$Country_Region == "Russia", "pop"] <- 145934462
df_all_cases[df_all_cases$Country_Region == "Ukraine", "pop"] <- 43733762
df_all_cases[df_all_cases$Country_Region == "Tajikistan", "pop"] <- 9537645
df_all_cases[df_all_cases$Country_Region == "Uzbekistan", "pop"] <- 33469203
df_all_cases[df_all_cases$Country_Region == "Korea, South", "pop"] <- 51290514
df_all_cases[df_all_cases$Country_Region == "Czechia", "pop"] <- 10718488
df_all_cases[df_all_cases$Country_Region == "Andorra", "pop"] <- 76177
df_all_cases[df_all_cases$Country_Region == "Antigua and Barbuda", "pop"] <- 96286
df_all_cases[df_all_cases$Country_Region == "Armenia", "pop"] <- 2970000
df_all_cases[df_all_cases$Country_Region == "Yemen", "pop"] <- 28500000
df_all_cases[df_all_cases$Country_Region == "Cyprus", "pop"] <- 876000

gdpPercap

df_all_cases[df_all_cases$Country_Region == "US", "gdpPercap"] <- 65297.5
df_all_cases[df_all_cases$Country_Region == "Bhutan", "gdpPercap"] <- 3316.2
df_all_cases[df_all_cases$Country_Region == "Brunei", "gdpPercap"] <- 31086.8
df_all_cases[df_all_cases$Country_Region == "Burma", "gdpPercap"] <- 1407.8
df_all_cases[df_all_cases$Country_Region == "Maldives", "gdpPercap"] <- 10626.5
df_all_cases[df_all_cases$Country_Region == "Qatar", "gdpPercap"] <- 62088.1
df_all_cases[df_all_cases$Country_Region == "United Arab Emirates", "gdpPercap"] <- 43103.3
df_all_cases[df_all_cases$Country_Region == "Taiwan*", "gdpPercap"] <- 26910.229
df_all_cases[df_all_cases$Country_Region == "Monaco", "gdpPercap"] <- 185829.0
df_all_cases[df_all_cases$Country_Region == "Luxembourg", "gdpPercap"] <- 114704.6
df_all_cases[df_all_cases$Country_Region == "Lithuania", "gdpPercap"] <- 19601.9
df_all_cases[df_all_cases$Country_Region == "Russia", "gdpPercap"] <- 11585.0
df_all_cases[df_all_cases$Country_Region == "Ukraine", "gdpPercap"] <- 3659.0
df_all_cases[df_all_cases$Country_Region == "Tajikistan", "gdpPercap"] <- 870.8
df_all_cases[df_all_cases$Country_Region == "Uzbekistan", "gdpPercap"] <- 1724.8
df_all_cases[df_all_cases$Country_Region == "Korea, South", "gdpPercap"] <- 31362.75
df_all_cases[df_all_cases$Country_Region == "Czechia", "gdpPercap"] <- 23078.57
df_all_cases[df_all_cases$Country_Region == "Andorra", "gdpPercap"] <- 42029.76
df_all_cases[df_all_cases$Country_Region == "Antigua and Barbuda", "gdpPercap"] <- 16726.98
df_all_cases[df_all_cases$Country_Region == "Armenia", "gdpPercap"] <- 4212.07
df_all_cases[df_all_cases$Country_Region == "Yemen", "gdpPercap"] <- 944.41
df_all_cases[df_all_cases$Country_Region == "Cyprus", "gdpPercap"] <- 28159.30

lifeExp

df_all_cases[df_all_cases$Country_Region == "US", "lifeExp"] <- 79.11
df_all_cases[df_all_cases$Country_Region == "Bhutan", "lifeExp"] <- 72.77
df_all_cases[df_all_cases$Country_Region == "Brunei", "lifeExp"] <- 76.35
df_all_cases[df_all_cases$Country_Region == "Burma", "lifeExp"] <- 67.78
df_all_cases[df_all_cases$Country_Region == "Maldives", "lifeExp"] <- 79.89
df_all_cases[df_all_cases$Country_Region == "Qatar", "lifeExp"] <- 80.73
df_all_cases[df_all_cases$Country_Region == "United Arab Emirates", "lifeExp"] <- 78.46
df_all_cases[df_all_cases$Country_Region == "Taiwan*", "lifeExp"] <- 81.04
df_all_cases[df_all_cases$Country_Region == "Monaco", "lifeExp"] <- 85.8
df_all_cases[df_all_cases$Country_Region == "Luxembourg", "lifeExp"] <- 82.79
df_all_cases[df_all_cases$Country_Region == "Lithuania", "lifeExp"] <- 76.41
df_all_cases[df_all_cases$Country_Region == "Russia", "lifeExp"] <- 72.99
df_all_cases[df_all_cases$Country_Region == "Ukraine", "lifeExp"] <- 72.50
df_all_cases[df_all_cases$Country_Region == "Tajikistan", "lifeExp"] <- 71.76
df_all_cases[df_all_cases$Country_Region == "Uzbekistan", "lifeExp"] <- 72.04
df_all_cases[df_all_cases$Country_Region == "Korea, South", "lifeExp"] <- 82.63
df_all_cases[df_all_cases$Country_Region == "Czechia", "lifeExp"] <- 79.48
df_all_cases[df_all_cases$Country_Region == "Andorra", "lifeExp"] <- 83
df_all_cases[df_all_cases$Country_Region == "Antigua and Barbuda", "lifeExp"] <- 76.75
df_all_cases[df_all_cases$Country_Region == "Armenia", "lifeExp"] <- 74.80
df_all_cases[df_all_cases$Country_Region == "Yemen", "lifeExp"] <- 66.09
df_all_cases[df_all_cases$Country_Region == "Cyprus", "lifeExp"] <- 80.67

Summarise

df_all_cases %>% 
  group_by(Country_Region, continent) %>% 
  summarise(Confirmed = sum(Confirmed),
            Active = sum(Active),
            Deaths = sum(Deaths),
            Recovered = sum(Recovered)) %>% 
  mutate(Case_Fatality_Ratio = Deaths * 100 / Confirmed)

continent_lookup <- df_all_cases %>% 
  select(Country_Region, continent)

df_all_cases %>% 
  group_by(Country_Region, continent) %>% 
  summarise(Confirmed = sum(Confirmed),
            Active = sum(Active),
            Deaths = sum(Deaths),
            Recovered = sum(Recovered)) %>% 
  ungroup() %>% 
  mutate(Case_Fatality_Ratio = Deaths * 100 / Confirmed) %>% 
  slice_max(order_by = Case_Fatality_Ratio, n = 15)
  # top_n( n = 15, wt = Case_Fatality_Ratio) %>% 
  # arrange(desc(Case_Fatality_Ratio))

df_all_cases_sum <- df_all_cases %>%
  group_by(Country_Region) %>% 
  summarise(continent = first(continent),
            pop = last(pop),
            gdpPercap = last(gdpPercap),
            Confirmed = sum(Confirmed),
            Active = sum(Active),
            Deaths = sum(Deaths),
            Recovered = sum(Recovered)) %>% 
  ungroup() %>% 
  mutate(Case_Fatality_Ratio = Deaths * 100 / Confirmed) %>% 
  arrange(desc(Confirmed))

df_all_cases_sum

df_all_cases %>% 
  select(Country_Region, pop, gdpPercap, lifeExp) %>% 
  distinct()

df_all_cases_sum %>% 
  ggplot(aes( x = Case_Fatality_Ratio, y = Confirmed)) +
  geom_point(aes(size = Confirmed, col = continent))

df_all_cases_sum %>% 
  ggplot(aes( x = Case_Fatality_Ratio, y = Confirmed)) +
  geom_point(aes(size = Confirmed, col = continent)) +
  facet_wrap(~continent) +
  theme_bw()

df_all_cases_sum %>% 
  filter(Case_Fatality_Ratio < 10) %>% 
  ggplot(aes( x = Case_Fatality_Ratio, y = Confirmed)) +
  geom_point(aes(size = Confirmed, col = continent))

df_all_cases_sum %>% 
  filter(Case_Fatality_Ratio < 10) %>% 
  ggplot(aes( x = Case_Fatality_Ratio, y = Confirmed)) +
  geom_point(aes(size = Confirmed, col = continent)) +
  theme_bw() +
  facet_wrap(~continent)

top_10 <- df_all_cases_sum %>% 
  top_n(n = 10, wt = Confirmed) %>% 
  pull(Country_Region)

top_10

top_20 <- df_all_cases_sum %>% 
  top_n(n = 20, wt = Confirmed) %>% 
  pull(Country_Region)

top_20

ts data

recovered ts

df_ts_recovered <- covid19.data("ts-recovered") 

df_ts_recovered %>% head()

confirmed ts

df_ts_confirmed <- covid19.data("ts-confirmed") 

df_ts_confirmed %>% head()

deaths ts

df_ts_deaths <- covid19.data("ts-deaths") 

df_ts_deaths %>% head()

df_ts_confirmed %>% select(last_col())

Long/ gather data

confirmed

df_gather_confirmed <-  gather(data = df_ts_confirmed, key = "Date", value = "Confirmed", -c("Province.State","Lat","Long", "Country.Region")) 

df_gather_confirmed %>% head()

recovered

df_gather_recovered <-  gather(data = df_ts_recovered, key = "Date", value = "Recovered", -c("Province.State","Lat","Long", "Country.Region")) 

df_gather_recovered %>% head()

deaths

df_gather_deaths <-  gather(data = df_ts_deaths, key = "Date", value = "Deaths", -c("Province.State","Lat","Long", "Country.Region")) 

df_gather_deaths %>% head()

Joining gathered data

grouped_cases_df <- df_gather_confirmed %>%  
  full_join(df_gather_recovered, by = c("Province.State", "Country.Region", 
                                        "Lat", "Long", "Date"))

grouped_cases_df

grouped_cases_df %>% 
  arrange(desc(Confirmed))

grouped_cases_df <- grouped_cases_df %>% 
  full_join(df_gather_deaths,by = c("Province.State", "Country.Region", "Lat", "Long", "Date"))

grouped_cases_df

adding columns

active cases

grouped_cases_df <- grouped_cases_df %>% 
  mutate(Active = Confirmed - (Recovered + Deaths),
         Date = as.Date(Date))

grouped_cases_df

CFR

grouped_cases_df <- grouped_cases_df %>% 
  mutate(Case_Fatality_Ratio = Deaths * 100 / Confirmed)

grouped_cases_df

data processing

data checks & cleaning

is.na(grouped_cases_df$Case_Fatality_Ratio) %>% head()

Replacing missing values with 0

grouped_cases_df[which(is.na(grouped_cases_df$Case_Fatality_Ratio)), "Case_Fatality_Ratio"] <- 0

grouped_cases_df <- grouped_cases_df %>% 
  mutate(Province.State = as.factor(Province.State),
         Country.Region = as.factor(Country.Region))

df_all_cases_sum %>% head()

Adding continent

grouped_cases_df <- left_join(x = grouped_cases_df,
          y = df_all_cases_sum %>% select(Country_Region, continent),
          by = c("Country.Region" = "Country_Region")
            )

grouped_cases_df

grouped_cases_df %>% head()

Creating grouped_province_cases_df

grouped_province_cases_df <- grouped_cases_df

grouped_province_cases_df

High CFR countries

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  count(n = Country.Region)

Adding CFR

grouped_cases_df <- grouped_cases_df %>% 
  select(Country.Region, continent, Date, Confirmed, Deaths, Active, Recovered) %>% 
  group_by(Country.Region, continent, Date) %>% 
  summarise(Confirmed = sum(Confirmed),
            Deaths = sum(Deaths),
            Active = sum(Active),
            Recovered = sum(Recovered)
            ) %>% 
  mutate(Case_Fatality_Ratio = Deaths / Confirmed * 100) %>% 
  ungroup()

grouped_cases_df

grouped_cases_df[which(is.na(grouped_cases_df$Case_Fatality_Ratio)), "Case_Fatality_Ratio"] <- 0

grouped_cases_df %>% head()

Adding Recovery Rate

grouped_cases_df <- grouped_cases_df %>% 
  mutate(Case_Fatality_Ratio = round(Case_Fatality_Ratio, digits = 2),
         Recovery_Rate = Recovered / Confirmed * 100) %>% 
  
  # Replacing NaN with 100 as when the cases are 0, recovery remains 0
  mutate(Recovery_Rate = replace(Recovery_Rate, is.na(Recovery_Rate), 0) %>% round(digits = 2)) 

grouped_cases_df

Adding Daily Cases

grouped_cases_df <- grouped_cases_df %>% 
  group_by(Country.Region, Date) %>% 
  summarise(continent = first(continent),
            Confirmed = sum(Confirmed),
            Deaths = sum(Deaths),
            Active = sum(Active),
            Recovered = sum(Recovered),
            Case_Fatality_Ratio = sum(Case_Fatality_Ratio),
            Recovery_Rate = sum(Recovery_Rate)) %>%
  mutate(Daily_cases = (Confirmed - lag(Confirmed, default = 0)),
         Daily_deaths = Deaths - lag(Deaths, default = 0),
         Daily_recovered = Recovered - lag(Recovered, default = 0)
            ) %>% 
  ungroup()

grouped_cases_df

Charts

CFR top 40

top 40

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  arrange(desc(Case_Fatality_Ratio)) %>% 
  head(n = 40) %>% 
  
  ggplot(aes(x = reorder(Country.Region, Case_Fatality_Ratio), 
             y = Case_Fatality_Ratio,
             fill = continent)) +
  geom_col() +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90) ) +
  coord_flip() +
  labs(title = "Top 40 CFR Countries",
       caption = "created by ViSa!!")

top 40 facet

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  arrange(desc(Case_Fatality_Ratio)) %>% 
  head(n = 40) %>% 
  
  ggplot(aes(x = reorder(Country.Region, Case_Fatality_Ratio), 
             y = Case_Fatality_Ratio,
             fill = continent)) +
  geom_col() +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90) ) +
  coord_flip() +
  facet_wrap(~continent) +
  labs(title = "Top 40 CFR Countries",
       caption = "created by ViSa!!")

all CFR line facet

grouped_cases_df %>% 
  
  ggplot(aes(x = Date, 
             y = Case_Fatality_Ratio,
             col = continent)) +
  geom_path() +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90) ) +
  facet_wrap(~continent) +
  labs(title = "CFR of all Countries for continent comparison",
       caption = "created by Visa!!")

all CFR line

grouped_cases_df %>% 
  
  ggplot(aes(x = Date, 
             y = Case_Fatality_Ratio,
             col = continent)) +
  geom_path() +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 90) ) +
  labs(title = "CFR of all Countries based on continent",
       caption = "created by Visa!!")

CFR top 10

Data Prep.

grouped_cases_df %>% head()

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  group_by(continent) %>% 
  
  top_n(n =10, wt = Case_Fatality_Ratio) %>% 
  ungroup() %>% 
  select(Country.Region, continent) %>% 
  unique() %>% 
  pull(continent) %>% 
  table()

top10_CFR <- grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  group_by(continent) %>% 
  
  top_n(n =10, wt = Case_Fatality_Ratio) %>% 
  ungroup() %>% 
  pull(Country.Region)

top10_CFR

plot

grouped_cases_df %>% 
  filter(Country.Region %in% top10_CFR) %>% 
  
  ggplot(aes(x = Date, y = Case_Fatality_Ratio, col = continent)) +
  geom_path() +
  theme_bw() +
  facet_wrap(~continent)

CFR_level colored line

India

Data Prep.

range(grouped_cases_df$Case_Fatality_Ratio)

quantiles use from: https://www.dummies.com/programming/r/how-to-check-quantiles-in-r/

quantile(grouped_cases_df$Case_Fatality_Ratio, probs = c(.33,.66,1), na.rm = TRUE )

quantile(grouped_cases_df$Case_Fatality_Ratio, probs = .33) 

grouped_cases_df$CFR_level <- cut(grouped_cases_df$Case_Fatality_Ratio, 
                                   breaks = c(-1,
                                              quantile(grouped_cases_df$Case_Fatality_Ratio, probs = .33),
                                              quantile(grouped_cases_df$Case_Fatality_Ratio, probs = .66),
                                              quantile(grouped_cases_df$Case_Fatality_Ratio, probs = 1)
                                              ),
                                   labels = c("Low","Med","High"))

grouped_cases_df$CFR_level <- cut(grouped_cases_df$Case_Fatality_Ratio, 
                                   breaks = c(-1,0.5004858,2.3189270,100),
                                   labels = c("Low","Med","High"))

grouped_cases_df %>% 
  filter(Country.Region == "India")

grouped_cases_df %>% 
  filter(Country.Region == "India") %>%
  select(CFR_level) %>% 
  table()

CFR_level plot 1

grouped_cases_df %>% 
  filter(Country.Region == "India") %>% 
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level)) +
  geom_path() +
  labs(title = "CFR level(Low,Med,High) of India wrt Time",
       caption = "created by ViSa!!")

duplicate check

grouped_cases_df %>% 
  filter(Country.Region == "India") %>% 
  count(Date) %>% 
  filter(n > 1)

grouped_cases_df %>% 
  filter(Country.Region == "India") %>% 
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level)) +
  geom_line() +
  labs(title = "CFR level(Low,Med,High) of India wrt Time",
       caption = "created by ViSa!!") 

Worked (group=1)

solution is to use group = 1 from: https://stackoverflow.com/questions/65422773/getting-multiple-lines-in-geom-line-time-graph-when-used-with-another-factor-c/65422835#65422835

grouped_cases_df %>% 
  filter(Country.Region == "India") %>% 
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level, group = 1)) +
  geom_line()  +
  labs(title = "CFR level(Low,Med,High) of India wrt Time",
       caption = "created by ViSa!!")

top 10

facet geom_line

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>% 
  
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level, group = 1)) +
  geom_line() +
  facet_wrap(~continent)

facet geom_path

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>% 
  
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level, group = 1)) +
  geom_path() +
  facet_wrap(~continent) +
  labs(title = "CFR level of top10 countries wrt Time split by continent",
       caption = "created by ViSa!!")

geom_line

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>% 
  
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level, group = 1)) +
  geom_line() #+

  # facet_wrap(~continent)

Final: geom_path

p1_cfr <- grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>% 
  
  ggplot(aes(x = Date, y = Confirmed, col = CFR_level, group = Country.Region, label = continent)) +
  geom_path(size = 1.2) +
  labs(title = "CFR level of top10 countries wrt Time",
       caption = "created by ViSa!!") 

p1_cfr

plotly & streamgraph

library(plotly)
library(streamgraph)

ggplotly(p1_cfr)

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>% 
  
  streamgraph(date = Date, key = "Country.Region", value = CFR_level) %>% 
  sg_fill_brewer("PuOr") %>% 
  sg_title(title = "CFR Level of top 10 countries wrt time") 

highcharts

library(highcharter)

heatmap

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, continent) %>% 
  summarise(Confirmed = sum(Confirmed)) %>% 
  arrange(desc(Confirmed)) %>% 
  
  hchart("heatmap", hcaes(x = Country.Region, y = continent, value = Confirmed, color = Confirmed)) %>% 
  hc_title(text = "Confirmed cases across globe split by continent")

treemap

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, continent) %>% 
  summarise(Confirmed = sum(Confirmed)) %>% 
  arrange(desc(Confirmed)) %>% 
  
  hchart("treemap", hcaes(x = Country.Region, value = Confirmed, color = Confirmed)) %>% 
  hc_title(text = "Confirmed cases across globe")

treemap colored

from: https://www.kaggle.com/nulldata/beginners-guide-to-highchart-visual-in-r

grouped_cases_df %>% 
  filter(Date == max(Date, na.rm = TRUE)) %>% 
  group_by(Country.Region, continent) %>% 
  summarise(Confirmed = sum(Confirmed)) %>% 
  arrange(desc(Confirmed)) %>% 
  
  hchart("treemap", hcaes(x = Country.Region, value = Confirmed, color = Confirmed)) %>% 
   hc_colorAxis(stops = color_stops(colors = viridis::inferno(10))) %>% 
  hc_title(text = "Confirmed cases across globe")

CFRlevel top10 color

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  head(n = 30)

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  summary()

grouped_cases_df %>% str()

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>% 
  str()

CFR based time line plots

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  
  hchart(type = "line", hcaes(x = Date, y = Confirmed, group = Country.Region, color = CFR_level)) %>% 
  hc_title(text = "Confirmed cases of top 10 countries across globe")

theme based highcharts

handdrawn

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  
  hchart(type = "line", hcaes(x = Date, y = Confirmed, group = Country.Region, color = CFR_level)) %>% 
  hc_add_theme(hc_theme_handdrawn()) %>% 
  hc_caption(text = "chart created by ViSa :)")

theme db

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  
  hchart(type = "line", hcaes(x = Date, y = Confirmed, group = Country.Region, color = CFR_level)) %>% 
  hc_add_theme(hc_theme_db()) %>% 
  hc_title(text = "Confirmed cases of top 10 countries across globe") %>% 
  hc_caption(text = "chart created by ViSa :)")

log plot theme smpl

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  
  hchart(type = "line", hcaes(x = Date, y = log(Confirmed), 
                              group = Country.Region, color = CFR_level)) %>% 
  hc_add_theme(hc_theme_smpl()) %>% 
  hc_title(text = "Confirmed cases of top 10 countries across globe") %>%
  hc_caption(text = "chart created by ViSa :)")

CFR based colored lines

grouped_cases_df %>% 
  filter(Country.Region %in% top_10) %>%
  
  hchart(type = "line", hcaes(x = Date, y = Confirmed, color = CFR_level)) %>% 
  hc_title(text = "Confirmed cases of top 10 countries across globe")

Solution 1

colored line in reply from

https://stackoverflow.com/questions/65427527/how-to-change-color-of-line-chart-in-highchart-based-on-a-categorical-column-in

grouped_cases_df %>% 
  filter(Country.Region %in% c("France","India")) %>%
  
  hchart(type = "coloredline", 
         hcaes(x = Date, y = Confirmed, group = Country.Region, segmentColor = CFR_level)) %>%
  hc_add_series(name = "Low", 
                color = "green", marker = list(symbol = "line")) %>% 
  
  hc_add_series(name = "Med", 
                color = "yellow", marker = list(symbol = "line")) %>% 
  
  hc_add_series(name = "High", 
                color = "red", marker = list(symbol = "line")) %>% 
  
  hc_add_dependency("plugins/multicolor_series.js")

Solution worked

from: https://stackoverflow.com/questions/65427527/how-to-change-color-of-line-chart-in-highchart-based-on-a-categorical-column-in https://jkunst.com/highcharter/articles/modules.html

grouped_cases_df %>% 
  rename(Country_Region = Country.Region) %>% 
  filter(Country_Region %in% top_10) %>%
  mutate(CFR_col = case_when(CFR_level == "Low" ~ "#DAF7A6",
                         CFR_level == "Med" ~ "#4f86b1",
                         TRUE ~ "#FFC300"),
         Case_Fatality_Ratio = round(Case_Fatality_Ratio, digits = 3)
         ) %>% 
  
  hchart(type = "coloredline", showInLegend = F,
         hcaes(x = Date, y = Confirmed, group = Country_Region, segmentColor = CFR_col)) %>%
  hc_add_dependency("plugins/multicolor_series.js") %>% 
  
  hc_tooltip(#shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}, {point.continent} <br> 
             CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>"
             ) %>%
  hc_title(text = "Top 10 Covid confirmed cases countries & colored with CFR Levels")  %>% 
  hc_caption(text = "chart created by ViSa :)")

Solution theme chalk

grouped_cases_df %>% 
  rename(Country_Region = Country.Region) %>% 
  filter(Country_Region %in% top_10) %>%
  mutate(CFR_col = case_when(CFR_level == "Low" ~ "#DAF7A6",
                         CFR_level == "Med" ~ "#4f86b1",
                         TRUE ~ "#FFC300"),
         Case_Fatality_Ratio = round(Case_Fatality_Ratio, digits = 3)
         ) %>% 
  
  hchart(type = "coloredline", showInLegend = F,
         hcaes(x = Date, y = Confirmed, group = Country_Region, segmentColor = CFR_col)) %>%
  hc_add_dependency("plugins/multicolor_series.js") %>% 
  
  hc_tooltip(#shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}, {point.continent} <br> 
             CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>"
             ) %>%
  hc_title(text = "Top 10 Covid confirmed cases countries & colored with CFR Levels") %>% 
  hc_add_theme(hc_theme_chalk()) %>% 
  hc_caption(text = "chart created by ViSa :)")

Solution theme monokai log

 grouped_cases_df %>% 
  rename(Country_Region = Country.Region) %>% 
  filter(Country_Region %in% top_10) %>%
  mutate(CFR_col = case_when(CFR_level == "Low" ~ "#DAF7A6",
                         CFR_level == "Med" ~ "#4f86b1",
                         TRUE ~ "#FFC300"),
         Case_Fatality_Ratio = round(Case_Fatality_Ratio, digits = 3)
         ) %>% 
  
  hchart(type = "coloredline", 
         hcaes(x = Date, y = Confirmed, group = Country_Region, segmentColor = CFR_col)) %>%
  hc_add_dependency("plugins/multicolor_series.js") %>% 
  
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}, {point.continent},  
             CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>"
             ) %>%
  hc_title(text = "Top 10 Covid confirmed cases countries & colored with CFR Levels") %>% 
  hc_add_theme(hc_theme_monokai()) %>%
  hc_yAxis(type = "logarithmic") %>% 
  hc_caption(text = "chart created by ViSa :)")

Long form data / df_stack

df_stack <- gather(data = grouped_cases_df, key = "Cases_type", value = Cases_count, 
       -c("Country.Region", "continent", Date, 
          Case_Fatality_Ratio, Recovery_Rate, CFR_level))

df_stack

table(df_stack$Cases_type)

df_stack <- df_stack %>% 
  
  mutate(Cases_type = as.factor(Cases_type),
         Cases_count = as.numeric(Cases_count),
         Date = as.Date(Date))

df_stack

df_stack %>% 
  filter(Date == max(Date, na.rm = TRUE))

df_stack <- df_stack %>% 
  mutate(Case_Fatality_Ratio = round(Case_Fatality_Ratio, digits = 2)) 

df_stack

Top N cases

top15 confirmed

top15_confirmed <- df_stack %>% 
  filter(Cases_type == "Confirmed",
         Date == max(Date, na.rm = TRUE)) %>%
  arrange(desc(Cases_count)) %>% 
  head(n = 15) %>% 
  pull(Country.Region)

top15_confirmed  

top15 active

top15_active <- df_stack %>% 
  filter(Cases_type == "Active",
         Date == max(Date, na.rm = TRUE)) %>%
  arrange(desc(Cases_count)) %>% 
  head(n = 15) %>% 
  pull(Country.Region)


top15_active  

Charts

All cases type

Based on Top 15 confirmed

df_stack %>%
  group_by(Country.Region, continent, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  ungroup() %>%
  filter(Country.Region %in% top15_confirmed,
         !Cases_type %in% c("Confirmed","Daily_cases","Daily_deaths","Daily_recovered"))

1 all cases type

df_stack %>%
  group_by(Country.Region, continent, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  ungroup() %>%
  filter(Country.Region %in% top15_confirmed,
         !Cases_type %in% c("Confirmed","Daily_cases","Daily_deaths","Daily_recovered")) %>%  
  
  ggplot(aes(x = reorder(Country.Region,Cases_count), 
             y = Cases_count, 
             fill = as.factor(Cases_type)
             )) +
  geom_col() +
  scale_y_continuous(labels = scales::comma_format()) +
  coord_flip() +
  labs(title = "All type cases of top countries",
       subtitle = "Top countries is based on Highest Confirmed cases till date",
       caption = "created by ViSa!!")

2 all cases type

df_stack %>%
  group_by(Country.Region, continent, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  ungroup() %>%
  filter(Country.Region %in% top15_confirmed,
         !Cases_type %in% c("Confirmed","Daily_cases","Daily_deaths","Daily_recovered")) %>% 
  
  ggplot(aes(x = reorder(Country.Region,Cases_count), 
             y = Cases_count,
             group = Country.Region, 
             fill = as.factor(Cases_type)
             )) +
  geom_col() +
  scale_y_continuous(labels = scales::comma_format()) +
  coord_flip() +
  labs(title = "All type cases of top countries",
       subtitle = "Top countries is based on Highest Confirmed cases till date",
       caption = "created by ViSa!!")

3 facet

df_stack %>%
  group_by(Country.Region, continent, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  ungroup() %>%
  filter(Country.Region %in% top15_confirmed,
         !Cases_type %in% c("Confirmed","Daily_cases","Daily_deaths","Daily_recovered")) %>% 
  
  ggplot(aes(x = reorder(Country.Region,Cases_count), 
             y = Cases_count, 
             fill = as.factor(Cases_type)
             )) +
  geom_col() +
  scale_y_continuous(labels = scales::comma_format()) +
  coord_flip() +
  facet_wrap(~continent) +
  labs(title = "All type cases of top countries",
       subtitle = "Top countries is based on Highest Confirmed cases till date",
       caption = "created by ViSa!!")

Active Cases

1 Active Cases

df_stack %>%
  group_by(Country.Region, continent, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  ungroup() %>%
  filter(Cases_type == "Active",
         Country.Region %in% top15_active) %>% 
  
  ggplot(aes(x = reorder(Country.Region,Cases_count), 
             y = Cases_count , 
             fill = as.factor(continent)
             )) +
  geom_col() +
  scale_y_continuous(labels = scales::comma_format()) +
  coord_flip()

scale_fill_tableau Active cases

from: https://www.kaggle.com/janiobachmann/time-series-feature-engineering-concepts

df_stack %>%
  group_by(Country.Region, continent, Date, Cases_type) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  ungroup() %>%
  filter(Cases_type == "Active",
         Country.Region %in% top15_active) %>% 
  
  ggplot(aes(x = reorder(Country.Region,Cases_count), 
             y = Cases_count , 
             fill = as.factor(continent)
             )) +
  geom_col() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_fill_tableau() +
  geom_label(aes(label = Cases_count), size=3, color="white") + 
  coord_flip()

Area Line Funnel plt

colored line plot from web

from: https://jkunst.com/highcharter/articles/modules.html

Area + line

set.seed(123)

n <- 200

colors <- sample(viridisLite::cividis(5, end = .9))

df <- tibble(
  x = 1:n,
  y = abs(arima.sim(n = n, model = list(ar = c(0.9)))) + 2,
  y2 = 10 + y,
  col = rep(colors, each = n/10, length.out = n)
)

hchart(df, "coloredarea", hcaes(x, y, segmentColor = col)) %>% 
   hc_add_series(df, "coloredline", hcaes(x, y2 , segmentColor = col)) %>% 
   hc_add_dependency("plugins/multicolor_series.js")

line

hchart(df, "coloredline", hcaes(x, y2, segmentColor = col)) %>% 
   # hc_add_series(df, "coloredline", hcaes(x, y2 , segmentColor = col)) %>% 
   hc_add_dependency("plugins/multicolor_series.js")

Funnel chart

cases in India

df_stack %>% 
  filter(Country.Region == "India",
         Date == max(Date, na.rm = TRUE)) %>% 
  
  hchart("funnel",
         hcaes(x = Cases_type, y = Cases_count)) %>% 
  hc_caption(text = "chart created by ViSa :)")

Bubble Charts

Bubble Chart

df_all_cases_sum %>% head()

pop,Confirmed,gdp

df_all_cases_sum %>% 
  hchart("scatter",
         hcaes(x = pop, y = Confirmed, size = gdpPercap, group = continent),
         maxSize = "15%") %>% 
  hc_caption(text = "chart created by ViSa :)")

gdp,Confirmed,pop

df_all_cases_sum %>% 
  hchart("scatter",
         hcaes(x = gdpPercap, y = Confirmed, size = pop, group = continent),
         maxSize = "15%") %>% 
  hc_caption(text = "chart created by ViSa :)")

added name

from: https://jkunst.com/blog/posts/2019-02-04-using-tooltips-in-unexpected-ways/

adding name = Country_Region to add it to tooltip but that doesnt work

df_all_cases_sum %>% 
  mutate(conf_to_pop = Confirmed / pop * 100) %>% 
  hchart("scatter",
         hcaes(x = gdpPercap, y = conf_to_pop, 
               size = pop, group = continent, name = Country_Region),
         maxSize = "15%") %>% 
  hc_title(text = "Ratio of Confirmed Cases to population Vs gdpPercap")  %>% 
  hc_caption(text = "chart created by ViSa :)")

tooltip, percent scale

from: https://paldhous.github.io/ucb/2016/dataviz/week13.html

yAxis to percent scale: https://www.highcharts.com/blog/tutorials/highcharts-for-r-users/

bubble_plt1 <- df_all_cases_sum %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2)) %>% 
  hchart("scatter",
         hcaes(x = gdpPercap, y = confirmed_to_pop, 
               size = pop, group = continent), 
         maxSize = "15%") %>% 
  hc_title(text = "Percentage of Covid Confirmed cases to population Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(labels = list(format = "{value}%")) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt1

saveplot

to save above plot

from: https://paldhous.github.io/ucb/2016/dataviz/week13.html

library(htmlwidgets)

saveWidget(bubble_plt1, "bubbleplt.html", selfcontained = TRUE, libdir = NULL, background = "white")

Quadrant lines

bubble_plt2 <- df_all_cases_sum %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2)) %>% 
  hchart("scatter",
         hcaes(x = gdpPercap, y = confirmed_to_pop, 
               size = pop, group = continent),
         maxSize = "15%") %>% 
  hc_title(text = "Percentage of Covid Confirmed cases to population Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(labels = list(format = "{value}%"),
            plotLines = list(
      list(
        label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = 7,
        # the zIndex is used to put the label text over the grid lines 
        zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(plotLines = list(
      list(
        label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = 90000,
        # the zIndex is used to put the label text over the grid lines 
        zIndex = 4
        )
      )
    ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt2

Annotation Reference

df_all_cases_sum %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2)) %>%
  filter(Country_Region == "Monaco")

from: https://jkunst.com/highcharter/reference/hc_annotations.html

# Ex 1
highchart() %>% 
  hc_add_series(
    data = c(29.9, 71.5, 106.4, 129.2, 144.0, 176.0, 135.6, 148.5, 216.4, 194.1, 95.6, 54.4)
  ) %>% 
  hc_xAxis(
    tickInterval = 0.5,
    gridLineWidth = 1  
  ) %>% 
  hc_annotations(
    list(
      labels = 
        list(
          list(
            point = list(x = 3, y = 129.2, xAxis = 0, yAxis = 0),
            text = "x: {x}<br/>y: {y}"
            ),
          list(
            point = list(x = 9, y = 194.1, xAxis = 0, yAxis = 0),
            text = "x: {x}<br/>y: {y}"
            ),
          list(
            point = list(x = 5, y = 100, xAxis = 0),
            text = "x: {x}<br/>y: {point.plotY} px"
            ),
          list(
            point = list(x = 0, y = 0),
            text = "x: {point.plotX} px<br/>y: {point.plotY} px"
            )
          )
      )
    )

# Ex 2
df <- data.frame(
  x = 1:10,
  y = 1:10
)

highchart() %>% 
  hc_add_series(data = df, hcaes(x = x, y = y), type = "area") %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(x = 5, y = 5, xAxis = 0, yAxis = 0), text = "Middle"),
        list(point = list(x = 1, y = 1, xAxis = 0, yAxis = 0), text = "Start")
      )
    )
  )

Bubble with Quadrant/Annotations

Added Annotations

plot_data <- df_all_cases_sum %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         xmedian =  median(gdpPercap, na.rm = TRUE),
         ymedian = median(confirmed_to_pop, na.rm = TRUE))
  
bubble_plt3 <-   hchart(plot_data, "scatter",
         hcaes(x = gdpPercap, y = confirmed_to_pop, 
               size = pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "Percentage of Covid Confirmed cases to population Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(labels = list(format = "{value}%"),
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymedian) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmedian)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_caption(text = "chart created by ViSa :)") #%>% 
  # hc_annotations(
  #   list(
  #     labels = list(
  #       list(point = list(x = 140000, y = 5.5, xAxis = 0, yAxis = 0), text = "Best Quadrant"),
  #       list(point = list(x = 140000, y = 13, xAxis = 0, yAxis = 0), text = "Rich with High cases Quadrant"),
  #       list(point = list(x = 45000, y = 13, xAxis = 0, yAxis = 0), text = "Worst Quadrant"),
  #       list(point = list(x = 45000, y = 5.5, xAxis = 0, yAxis = 0), text = "World Quadrant")
  #     )
  #   )
  # )

bubble_plt3

saveWidget(bubble_plt3, "bubble_annotation_plt.html", selfcontained = TRUE, libdir = NULL, background = "white")

dynamic Quadrant data

df_all_cases_sum %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         xmedian =  mean(gdpPercap, na.rm = TRUE),
         ymedian = mean(confirmed_to_pop, na.rm = TRUE))

dynamic quadrant plt

plot_data <- df_all_cases_sum %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         xmid =  max(gdpPercap, na.rm = TRUE)/2,
         ymid = max(confirmed_to_pop, na.rm = TRUE)/2)
  
bubble_plt4 <-   hchart(plot_data, "scatter",
         hcaes(x = gdpPercap, y = confirmed_to_pop, 
               size = pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "Percentage of Covid Confirmed cases to population Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(labels = list(format = "{value}%"),
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymid) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmid)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(x = unique(plot_data$xmid)*1.5, y = 5.5, xAxis = 0, yAxis = 0), text = "Best Quadrant"),
        list(point = list(x = unique(plot_data$xmid)*1.5, y = 13, xAxis = 0, yAxis = 0), text = "Rich with High cases Quadrant"),
        list(point = list(x = unique(plot_data$xmid)*.5, y = 13, xAxis = 0, yAxis = 0), text = "Worst Quadrant"),
        list(point = list(x = unique(plot_data$xmid)*.5, y = 5.5, xAxis = 0, yAxis = 0), text = "World Quadrant")
      )
    )
  ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt4

Bubble plt (-Monaco)

Size based on population

-Monaco

Data/Plot Without Monaco

plot_data <- df_all_cases_sum %>% filter(Country_Region != "Monaco") %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         xmid =  max(gdpPercap, na.rm = TRUE)/2,
         ymid = max(confirmed_to_pop, na.rm = TRUE)/2)
  
bubble_plt5 <-   hchart(plot_data, "scatter",
         hcaes(x = gdpPercap, y = confirmed_to_pop, 
               size = pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "Percentage of Covid Confirmed cases to population Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(labels = list(format = "{value}%"),
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymid) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmid)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(x = unique(plot_data$xmid)*1.5, y = 5.5, xAxis = 0, yAxis = 0), text = "Best Quadrant"),
        list(point = list(x = unique(plot_data$xmid)*1.5, y = 13, xAxis = 0, yAxis = 0), text = "Rich with High cases Quadrant"),
        list(point = list(x = unique(plot_data$xmid)*.5, y = 13, xAxis = 0, yAxis = 0), text = "Worst Quadrant"),
        list(point = list(x = unique(plot_data$xmid)*.5, y = 5.5, xAxis = 0, yAxis = 0), text = "World Quadrant")
      )
    )
  ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt5

Reverse Quadrant

Reversing Quadrants / axis

plot_data <- df_all_cases_sum %>% filter(Country_Region != "Monaco") %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         ymid =  max(gdpPercap, na.rm = TRUE)/2,
         xmid = max(confirmed_to_pop, na.rm = TRUE)/2)
  
bubble_plt_reversed_6 <-   hchart(plot_data, "scatter",
         hcaes(y = gdpPercap, x = confirmed_to_pop, 
               size = pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "Covid Confirmed cases to population (%) Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymid) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(labels = list(format = "{value}%"),
           plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmid)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(y = unique(plot_data$ymid)*.7, x = 9.5, xAxis = 0, yAxis = 0), text = "Worst Quadrant"),
        list(point = list(y = unique(plot_data$ymid)*1.8, x = 9.5, xAxis = 0, yAxis = 0), text = "Rich with High cases Quadrant"),
        list(point = list(y = unique(plot_data$ymid)*.7, x = 3, xAxis = 0, yAxis = 0), text = "World Quadrant"),
        list(point = list(y = unique(plot_data$ymid)*1.8, x = 3, xAxis = 0, yAxis = 0), text = "Best Quadrant")
      )
    )
  ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt_reversed_6

more

df_all_cases_sum %>% 
  filter(str_detect(str_to_lower(Country_Region), "vietnam"))

plot_data <- df_all_cases_sum %>% #filter(Country_Region != "Monaco") %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         ymid =  max(gdpPercap, na.rm = TRUE)/2,
         xmid = max(confirmed_to_pop, na.rm = TRUE)/2)
  
bubble_plt_reversed_7 <-   hchart(plot_data, "scatter",
         hcaes(y = gdpPercap, x = confirmed_to_pop, 
               size = pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "Covid Confirmed cases to population (%) Vs gdpPercap") %>%
  hc_subtitle(text = "Bubble size relative to size of population") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymid) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(labels = list(format = "{value}%"),
           plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmid)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(y = unique(plot_data$ymid)*.7, x = 9.5, xAxis = 0, yAxis = 0), text = "Worst Quadrant"),
        list(point = list(y = unique(plot_data$ymid)*1.95, x = 9.5, xAxis = 0, yAxis = 0), text = "Rich with High cases Quadrant"),
        list(point = list(y = unique(plot_data$ymid)*.7, x = 3, xAxis = 0, yAxis = 0), text = "World Quadrant"),
        list(point = list(y = unique(plot_data$ymid)*1.95, x = 3, xAxis = 0, yAxis = 0), text = "Best Quadrant")
      )
    )
  ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt_reversed_7

saveWidget(bubble_plt_reversed_7, "bubble_reversed_plt.html", selfcontained = TRUE, libdir = NULL, background = "white")

gdpPercap Vs pop

Size based on (Confirmed cases / population)

1 All

plot_data <- df_all_cases_sum %>% #filter(Country_Region != "Monaco") %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         ymid =  max(gdpPercap, na.rm = TRUE)/2,
         xmid = max(pop, na.rm = TRUE)/2)  
  
bubble_plt_reversed_8 <-   hchart(plot_data, "scatter",
         hcaes(y = gdpPercap, x = pop, 
               size = confirmed_to_pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "gdpPercap Vs population ") %>%
  hc_subtitle(text = "Bubble size relative to confirmed cases/population ratio") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymid) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(#labels = list(format = "{value}%"),
           plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmid)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(y = unique(plot_data$ymid)*.7, x = unique(plot_data$xmid)*1.5, 
                          xAxis = 0, yAxis = 0), text = "High Pop with low gdpPercap Quadrant"),
        
        list(point = list(y = unique(plot_data$ymid)*2.5, x = unique(plot_data$xmid)*.5, 
                          xAxis = 0, yAxis = 0), text = "Rich with High gdpPercap Quadrant"),
        
        list(point = list(y = unique(plot_data$ymid)*.7, x = unique(plot_data$xmid)*.5, 
                          xAxis = 0, yAxis = 0), text = "World Quadrant"),
        
        list(point = list(y = unique(plot_data$ymid)*2.5, x = unique(plot_data$xmid)*1.5, 
                          xAxis = 0, yAxis = 0), text = "High Pop & gdpPercap")
      )
    )
  ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt_reversed_8

saveWidget(bubble_plt_reversed_8, "bubble_gdpVspop_plt.html", selfcontained = TRUE, libdir = NULL, background = "white")

2 (-Monaco)

plot_data <- df_all_cases_sum %>% filter(!Country_Region %in% c("Monaco","Luxembourg")) %>% 
  mutate(confirmed_to_pop = round(Confirmed / pop * 100, digits = 2),
         ymid =  max(gdpPercap, na.rm = TRUE)/2,
         xmid = max(pop, na.rm = TRUE)/2)  
  
bubble_plt_reversed_9 <-   hchart(plot_data, "scatter",
         hcaes(y = gdpPercap, x = pop, 
               size = confirmed_to_pop, group = continent),
         maxSize = "15%") %>% 
  
  hc_title(text = "gdpPercap Vs population ") %>%
  hc_subtitle(text = "Bubble size relative to confirmed cases/population ratio") %>%
  hc_tooltip(shared = TRUE,
             # borderColor = "black",
             pointFormat = "{point.Country_Region}<br> 
             Confirmed cases to population: {point.confirmed_to_pop}%<br> 
             pop: {point.pop}, gdpPercap: {point.gdpPercap} <br>"
             ) %>% 
   hc_yAxis(
            plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value = unique(plot_data$ymid) #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 1
        )
      )
            ) %>% 
  hc_xAxis(#labels = list(format = "{value}%"),
           plotLines = list(
      list(
        # label = list(text = "This is a plotLine"),
        color = "grey",
        width = 2,
        value =  unique(plot_data$xmid)  #,
        # the zIndex is used to put the label text over the grid lines 
        # zIndex = 4
        )
      )
    ) %>% 
  hc_annotations(
    list(
      labels = list(
        list(point = list(y = unique(plot_data$ymid)*.7, x = unique(plot_data$xmid)*1.5, 
                          xAxis = 0, yAxis = 0), text = "High Pop with low gdpPercap Quadrant"),
        
        list(point = list(y = unique(plot_data$ymid)*2, x = unique(plot_data$xmid)*.5, 
                          xAxis = 0, yAxis = 0), text = "Rich with High gdpPercap Quadrant"),
        
        list(point = list(y = unique(plot_data$ymid)*.7, x = unique(plot_data$xmid)*.5, 
                          xAxis = 0, yAxis = 0), text = "World Quadrant"),
        
        list(point = list(y = unique(plot_data$ymid)*2, x = unique(plot_data$xmid)*1.5, 
                          xAxis = 0, yAxis = 0), text = "High Pop & gdpPercap")
      )
    )
  ) %>% 
  hc_caption(text = "chart created by ViSa :)")

bubble_plt_reversed_9

Sunburst plots

sunburst

from: https://jkunst.com/highcharter/reference/data_to_hierarchical.html

Top 20

df_all_cases_sum %>% 
  filter(Country_Region %in% top_20) %>% 
  
  highcharter::data_to_hierarchical(c(continent, Country_Region), Confirmed) %>% 
  
  hchart(type = "sunburst")

Top 20 virdis colors

library(viridisLite)

cols_virdis <- viridis(4)
cols_virdis <- substr(cols_virdis, 0, 7)

cols_virdis

df_all_cases_sum %>% 
  filter(Country_Region %in% top_20) %>% 
  
  highcharter::data_to_hierarchical(
    group_vars = c(continent, Country_Region), 
    size_var = Confirmed,
    # colors = getOption("hicharter.color_palette")
    colors = cols_virdis) %>% 
  
  hchart(type = "sunburst")

Top 20 RColorBrewer

from: https://www.datanovia.com/en/blog/the-a-z-of-rcolorbrewer-palette/

library(RColorBrewer)

display.brewer.all()

col_selected <- RColorBrewer::display.brewer.all(n = 4, select = "Set3")

col_selected

display.brewer.pal(n = 4, name = "Set2")

col_selected <- brewer.pal(n =4, name = "Set2")
col_selected

RcolorBrew Sunburst

df_all_cases_sum %>% 
  filter(Country_Region %in% top_20) %>% 
  
  highcharter::data_to_hierarchical(
    group_vars = c(continent, Country_Region), 
    size_var = Confirmed,
    # colors = getOption("hicharter.color_palette")
    colors = col_selected) %>% 
  
  hchart(type = "sunburst")

World top 20

df_all_cases_sum %>% 
  filter(Country_Region %in% top_20) %>% 
  mutate(world = "World top 20 confirmed cases") %>% 
  
  highcharter::data_to_hierarchical(
    group_vars = c(world, continent, Country_Region), 
    size_var = Confirmed,
    # colors = getOption("hicharter.color_palette")
    colors = col_selected) %>% 
  
  hchart(type = "sunburst")

treemap

df_all_cases_sum %>% 
  filter(Country_Region %in% top_20) %>% 
  
  highcharter::data_to_hierarchical(
    group_vars = c(continent, Country_Region), 
    size_var = Confirmed,
    # colors = getOption("hicharter.color_palette")
    colors = col_selected) %>% 
  
  hchart(type = "treemap")

Top 10

df_all_cases_sum %>% 
  filter(Country_Region %in% top_10) %>% 
  
  highcharter::data_to_hierarchical(
    group_vars = c(continent, Country_Region), 
    size_var = Confirmed,
    # colors = getOption("hicharter.color_palette")
    colors = col_selected) %>% 
  
  hchart(type = "sunburst")

sunburst plotly

df_all_cases_sum %>% 
  filter(Country_Region == top_20) %>%
  
  plot_ly(data = .,
          labels = .$Country_Region, 
          parents = .$continent, 
          values = .$Confirmed,
          type = "sunburst"
          )

Top N dot plots

Cross table dot plots

from: https://www.kaggle.com/anshumoudgil/untying-climate-s-knots-visualisation

df_all_cases_sum %>% head()

Top 10 Confirmed

df_all_cases_sum %>% 
  filter(Country_Region %in% top_10) %>% 
  
  ggplot(aes(x = Country_Region, y = continent)) +
  geom_point(aes(size = Confirmed), shape = 16, color = "red")

Top 10 Confirmed (white col)

df_all_cases_sum %>% 
  filter(Country_Region %in% top_10) %>% 
  
  ggplot(aes(x = Country_Region, y = continent)) +
  geom_point(size = 5, shape = 19, color = "red") +
  geom_point(size = 2, shape = 16, color = "white") +
  theme_minimal()

Top n countries across months

df_stack %>% head()

df_stack %>% 
  select(Country.Region, continent) %>% 
  unique()

df_stack %>% 
  mutate(month = month(Date)) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 10, wt = Cases_count)

df_stack %>% 
  mutate(month = month(Date)) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 10, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region")

Top 5 basic

df_stack %>% 
  mutate(month = month(Date)) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = Country.Region, y = continent)) +
  geom_point(size = 6, shape = 19, color = "midnightblue") +
  geom_point(size = 2, shape = 16, color = "white") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5: 1 geom_point

df_stack %>% 
  mutate(month = month(Date)) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = Country.Region, y = continent)) +
  geom_point(size = 3, shape = 21, color = "midnightblue", fill = "white", stroke = 3) +
  # geom_point(size = 2, shape = 16, color = "white") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5: colored months

df_stack %>% 
  # mutate(month = lubridate::month(Date, label = TRUE, abbr = TRUE)) %>%
  mutate(month = format(Date, "%Y-%m")) %>% 
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = Country.Region, y = continent)) +
  geom_point(size = 3, shape = 21, aes(color = as.factor(month)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5: colored continents

df_stack %>% 
  # mutate(month = lubridate::month(Date, label = TRUE, abbr = TRUE)) %>%
  mutate(month = format(Date, "%Y-%m")) %>% 
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = Country.Region, y = continent)) +
  geom_point(size = 3, shape = 21, aes(color = as.factor(continent)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5 fliped axis

Top 5 colored months

df_stack %>% 
  # mutate(month = lubridate::month(Date, label = TRUE, abbr = TRUE)) %>%
  mutate(month = format(Date, "%Y-%m")) %>% 
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = continent, y = Country.Region)) +
  geom_point(size = 3, shape = 21, aes(color = as.factor(month)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5 colored continent

df_stack %>% 
  # mutate(month = lubridate::month(Date, label = TRUE, abbr = TRUE)) %>%
  mutate(month = format(Date, "%Y-%m")) %>% 
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = continent, y = Country.Region)) +
  geom_point(size = 3, shape = 21, aes(color = as.factor(continent)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5 (size on Cases_count)

size varies based on whole data because of ungroup

df_stack %>% 
  # mutate(month = lubridate::month(Date, label = TRUE, abbr = TRUE)) %>%
  mutate(month = format(Date, "%Y-%m")) %>% 
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  ungroup() %>% 
  
  left_join(y = df_stack %>% 
              select(Country.Region, continent) %>% 
              unique(),
            by = "Country.Region") %>% 
  
  ggplot(aes(x = continent, y = Country.Region)) +
  geom_point(shape = 21, aes(size = Cases_count, color = as.factor(continent)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  labs(title = "Top 5 confirmed cases Countries across the year")

Top 5 (size on Cases_count)

size varies based on grouped monthly data

Solution from: https://stackoverflow.com/questions/65456595/how-to-group-or-nest-to-a-subset-of-the-data-in-r/65482285#65482285

df_stack %>% 
  # mutate(month = lubridate::month(Date, label = TRUE, abbr = TRUE)) %>%
  mutate(month = format(Date, "%Y-%m")) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month, Country.Region) %>% 
  dplyr::summarise(Cases_count = sum(Cases_count, na.rm = TRUE),
            continent = first(continent)) %>% 
  top_n(n = 5, wt = Cases_count) %>%
  group_by(month) %>%
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>%
ggplot(aes(x = continent, y = Country.Region)) +
  geom_point(shape = 21, aes(size = Cases_size, color = as.factor(continent)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  coord_cartesian(clip = "off") +
  scale_color_tableau() +
  labs(title = "Top 5 confirmed cases Countries based on each month")

unsorted months_yr

df_stack %>% 
  mutate(month_yr = paste0(lubridate::month(Date, label = TRUE, abbr = TRUE),"_",lubridate::year(Date)
         # ),
         # 
         # # to set order of month factors
         # month_yr = as.factor(month_yr, levels = c("Jan_2020","Feb_2020","Mar_2020",
         #                                            "Apr_2020","May_2020","Jun_2020",
         #                                            "Jul_2020","Aug_2020","Sep_2020",
         #                                            "Oct_2020","Nov_2020","Dec_2020",
         #                                            "Jan_2021")
         )) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month_yr, Country.Region) %>% 
  
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE),
            continent = first(continent)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  group_by(month_yr) %>% #pull(month_yr) %>% unique()
  
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  
  ggplot(aes(x = continent, y = Country.Region)) +
  geom_point(shape = 21, # alpha = 0.6, 
             aes(size = Cases_size, color = as.factor(continent)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month_yr) +
  coord_cartesian(clip = "off") +
  scale_color_tableau() +
  labs(title = "Top 5 confirmed cases Countries based on each month")

sorted yr-month

to handle issue of month to month_yr:

solution from: https://stackoverflow.com/questions/65584432/how-to-custom-sort-data-based-on-months-data-column-in-r?noredirect=1#comment115955396_65584432

df_stack %>% 
  mutate(month_yr = format(Date, "%Y-%m")) %>%
  filter(Cases_type == "Confirmed") %>% 
  group_by(month_yr, Country.Region) %>% 
  
  summarise(Cases_count = sum(Cases_count, na.rm = TRUE),
            continent = first(continent)) %>% 
  top_n(n = 5, wt = Cases_count) %>% 
  group_by(month_yr) %>% #pull(month_yr) %>% unique()
  
  mutate(Cases_size = Cases_count / sum(Cases_count)) %>% 
  
  ggplot(aes(x = continent, y = Country.Region)) +
  geom_point(shape = 21, # alpha = 0.6, 
             aes(size = Cases_size, color = as.factor(continent)), fill = "white", stroke = 3) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month_yr) +
  coord_cartesian(clip = "off") +
  scale_color_tableau() +
  labs(title = "Top 5 confirmed cases Countries based on each month")

Rate Plots

India cases timeseries

https://prsindia.org/covid-19/cases

base plot

cases_ts_India <- df_stack %>% 
  filter(Country.Region == "India") %>% 
  
  ggplot(aes(x = Date, y = Cases_count, col = Cases_type)) +
  geom_path()

cases_ts_India

plotly plot

ggplotly(cases_ts_India)

bottom legend

ggplotly(cases_ts_India) %>% 
  layout(legend = list(orientation = "h", y =-0.2))

positioning legend

from: https://stackoverflow.com/questions/40001518/add-title-to-the-plotly-legend https://stackoverflow.com/questions/65466286/how-to-adjust-legend-title-position-in-ggplotly-in-r?noredirect=1#comment115742429_65466286

cases_ts_India <- cases_ts_India + 
  theme( legend.title = element_blank())

legendtitle <- list(yref='paper',xref="paper",y=-.28, x=0.06, text="Cases_type",showarrow=F)

ggplotly(cases_ts_India) %>% 
  layout(title = "India Cases types over the year ",
         legend = list(orientation = "h", x =0.2, y = -0.2),
         annotations = legendtitle)  

Recovery_Rate + CFR plts

Basic plt

rates_ind_plot <- grouped_cases_df %>% 
  filter(Country.Region == "India") %>% 
  select(Country.Region, Date, Case_Fatality_Ratio, Recovery_Rate) %>% 
  gather(key = "Ratio_type", value = Ratio_value, -c(Country.Region, Date)) %>% 
  
  ggplot(aes(x = Date, y = Ratio_value, col = Ratio_type)) +
  geom_path(size = 1.2) 

rates_ind_plot

plotly plt

rates_ind_plot <- rates_ind_plot + theme( legend.title = element_blank())

legendtitle <- list(yref='paper',xref="paper",y=-.28, x=0.06, text="Ratio_type",showarrow=F)

ggplotly(rates_ind_plot) %>% 
  layout(title = "India Cases rates over the year ",
         legend = list(orientation = "h", x =0.2, y = -0.2),
         annotations = legendtitle)

Treemap

treemap: Cases

Confirmed

df_all_cases_sum %>% 
  hchart(type = "treemap",
         hcaes(x = Country_Region, value = Confirmed, color = Confirmed)) %>% 
  hc_colorAxis(stops = color_stops(colors = viridis::inferno(10))) %>% 
  hc_title(text = "Relative Size of Cumulative confirmed Cases of countries")

Active

df_all_cases_sum %>% 
  hchart(type = "treemap",
         hcaes(x = Country_Region, value = Active, color = Active)) %>% 
  hc_colorAxis(stops = color_stops(colors = viridis::inferno(10))) %>% 
  hc_title(text = "Relative Size of Current Active Cases of countries")

Top N Line plots

Line Plots- Active, Daily

Top15 Active

grouped_cases_df %>% 
  filter(Country.Region %in% (df_all_cases_sum %>% 
                                top_n(n = 15, wt = Active) %>% 
                                pull(Country_Region)) 
         ) %>% 
  ggplot(aes(x = Date, y = Active, col = continent)) +
  geom_path() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_tableau() 

  # geom_label(aes(label = Active), size=3, color="white")

Top15 Active facet

top15_Active_plt <- grouped_cases_df %>% 
  filter(Country.Region != "US",
         Country.Region %in% (df_all_cases_sum %>% 
                                top_n(n = 15, wt = Active) %>% 
                                pull(Country_Region)) 
         ) %>% 
  ggplot(aes(x = Date, y = Active, col = Country.Region)) +
  geom_line() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_tableau(palette = "Tableau 20") +
  facet_wrap(~continent) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Top 15 Countries(-US) with Active Cases",
       caption = "created by ViSa!!")

top15_Active_plt  

+ plotly

ggplotly(top15_Active_plt) %>% 
  layout(legend = list(orientation = "h", y = -0.2))

Top15 Active hc_theme

grouped_cases_df %>% 
  filter(Country.Region != "US",
         Country.Region %in% (df_all_cases_sum %>% 
                                top_n(n = 15, wt = Active) %>% 
                                pull(Country_Region)) 
         ) %>% 
  rename(Country_Region = Country.Region) %>% 
  
  hchart(type = "line", 
         hcaes(x = Date, y = Active, group = Country_Region)) %>% 
  
  hc_tooltip(
    # shared = TRUE,
             pointFormat = "{point.Country_Region}, {point.Active} <br>"  
             # CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>
             ) %>%
  hc_title(text = "Top 15 Covid Active cases countries") %>% 
  hc_add_theme(hc_theme_monokai()) %>%
  # hc_yAxis(type = "logarithmic") %>% 
  hc_caption(text = "chart created by ViSa :)")

Top15 Active hc_theme +log

grouped_cases_df %>% 
  filter(Country.Region != "US",
         Country.Region %in% (df_all_cases_sum %>% 
                                top_n(n = 15, wt = Active) %>% 
                                pull(Country_Region)) 
         ) %>% 
  rename(Country_Region = Country.Region) %>% 
  
  hchart(type = "line", 
         hcaes(x = Date, y = Active, group = Country_Region)) %>% 
  
  hc_tooltip(
    # shared = TRUE,
             pointFormat = "{point.Country_Region}, {point.Active} <br>"  
             # CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>
             ) %>%
  hc_title(text = "Log of Top 15 Covid Active cases countries") %>% 
  hc_add_theme(hc_theme_monokai()) %>%
  hc_yAxis(type = "logarithmic") %>%
  hc_caption(text = "chart created by ViSa :)")

Top15 Line plt Confirmed

Active growth of Top Confirmed

grouped_cases_df %>% 
  filter(Country.Region != "US",
         Country.Region %in% (df_all_cases_sum %>% 
                                top_n(n = 15, wt = Confirmed) %>% 
                                pull(Country_Region)) 
         ) %>% 
  rename(Country_Region = Country.Region) %>% 
  
  hchart(type = "line", 
         hcaes(x = Date, y = Active, group = Country_Region)) %>% 
  
  hc_tooltip(
    # shared = TRUE,
             pointFormat = "{point.Country_Region}, {point.Active} <br>"  
             # CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>
             ) %>%
  hc_title(text = "Active cases growth of Top 15 Covid Confirmed countries") %>% 
  hc_add_theme(hc_theme_monokai()) %>%
  # hc_yAxis(type = "logarithmic") %>% 
  hc_caption(text = "chart created by ViSa :)")

Top Daily Cases + (-US,Turkey)

grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                slice_max(order_by = Daily_cases, n = 15) %>% 
                                pull(Country.Region)) 
         ) %>% 
  rename(Country_Region = Country.Region) %>% 
  
  hchart(type = "line", 
         hcaes(x = Date, y = Daily_cases, group = Country_Region)) %>% 
  
  hc_tooltip(
    # shared = TRUE,
             pointFormat = "{point.Country_Region}, {point.Daily_cases} <br>"  
             # CFR Level: {point.CFR_level}, {point.Case_Fatality_Ratio} <br>
             ) %>%
  hc_title(text = "Top 15 Covid Daily cases countries as of latest date") %>% 
  hc_caption(text = "chart created by ViSa!!") %>% 
  hc_add_theme(hc_theme_monokai())
  # hc_yAxis(type = "logarithmic") %>% 

+ colored CFRlevel

grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                slice_max(order_by = Daily_cases, n = 15) %>% 
                                pull(Country.Region)) 
         ) %>% 
  mutate(CFR_col = case_when(CFR_level == "Low" ~ "#DAF7A6",
                         CFR_level == "Med" ~ "#4f86b1",
                         TRUE ~ "#FFC300")
         ) %>% 
  
  rename(Country_Region = Country.Region) %>% 
  
  hchart(type = "coloredline", 
         hcaes(x = Date, y = Daily_cases, group = Country_Region, segmentColor = CFR_col)) %>% 
  hc_add_dependency("plugins/multicolor_series.js") %>% 
  
  hc_tooltip(
    shared = TRUE,
             pointFormat = "{point.Country_Region}, CFR Level: {point.CFR_level},
                            {point.Case_Fatality_Ratio},Daily_case: {point.Daily_cases} <br>"
             ) %>%
  hc_title(text = "Top 15 Covid Daily cases cases countries") %>% 
  hc_subtitle(text = "Colored on basis of CFR level") %>% 
  hc_caption(text = "chart created by ViSa!!") %>% 
  hc_add_theme(hc_theme_monokai())
  # hc_yAxis(type = "logarithmic") %>% 

Top N Plots

+gghighlight (Top5)

grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey")) %>% 
  rename(Country_Region = Country.Region) %>% 
  
  ggplot(aes(x = Date, y = Daily_cases, col = Country_Region)) +
  geom_line(size = 0.8) +
  scale_color_tableau(palette = "Tableau 10") +
  theme_minimal() +
  gghighlight(Country_Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                slice_max(order_by = Daily_cases, n = 5) %>% 
                                pull(Country.Region))) +
  labs(title = "Top 5 Covid Daily cases cases countries (-US,Turkey)",
       subtitle = "Highlighted for Top Countries",
       caption = "created by ViSa !!")

+Facet

grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                arrange(desc(Daily_cases)) %>% 
                                head(n = 15) %>% 
                                pull(Country.Region)) 
         ) %>% 
  ggplot(aes(x = Date, y = Daily_cases, col = Country.Region)) +
  geom_line() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_tableau(palette = "Tableau 20") +
  facet_wrap(~continent) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Top 15 Countries(-US) with Daily Cases",
       caption = "created by ViSa!!")

+plotly

ggplotly(grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                arrange(desc(Daily_cases)) %>% 
                                head(n = 15) %>% 
                                pull(Country.Region)) 
         ) %>% 
  ggplot(aes(x = Date, y = Daily_cases, col = Country.Region)) +
  geom_line() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_tableau(palette = "Tableau 20") +
  facet_wrap(~continent) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Top 15 Countries(-US) with Daily Cases",
       caption = "created by ViSa!!")
  ) %>% 
  layout(legend = list(orientation = "h", y = -0.2))

plotly (-facet)

ggplotly(grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                arrange(desc(Daily_cases)) %>% 
                                head(n = 15) %>% 
                                pull(Country.Region)) 
         ) %>% 
  ggplot(aes(x = Date, y = Daily_cases, col = Country.Region)) +
  geom_line() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_tableau(palette = "Tableau 20") +
  # facet_wrap(~continent) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Top 15 Countries(-US) with Daily Cases",
       caption = "created by ViSa!!")
  ) %>% 
  layout(legend = list(orientation = "h", y = -0.2))

Top10 European

ggplotly(
  grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE),
                                       continent == "Europe") %>% 
                                slice_max(order_by = Daily_cases, n = 10) %>% 
                                pull(Country.Region)) 
         ) %>% 
  ggplot(aes(x = Date, y = Daily_cases, col = Country.Region)) +
  geom_line() +
  scale_y_continuous(labels = scales::comma_format()) +
  scale_color_tableau(palette = "Tableau 20") +
  # facet_wrap(~continent) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "Top 10 Countries from Europe(-Turkey) with Daily Cases",
       caption = "created by ViSa!!")

)%>% 
  layout(legend = list(orientation = "h", y = -0.2))  

Top10 European: streamgraph

grouped_cases_df %>% 
  filter(!Country.Region %in% c("US","Turkey"),
         Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE),
                                       continent == "Europe") %>% 
                                slice_max(order_by = Daily_cases, n = 10) %>% 
                                pull(Country.Region)) 
         ) %>% 
  streamgraph(date = "Date", value = "Daily_cases", key = "Country.Region") %>% 
  sg_fill_tableau() %>% 
  sg_title(title = "Top 10 Countries from Europe(-Turkey) with Daily Cases") 

TopN Barrs, Lines facet

Top Daily cases sum Monthly

grouped_cases_df %>% 
    
  filter(Date > as.Date("2020-02-29")) %>% 
  mutate(month = format(Date, "%Y-%m")) %>%
  
  group_by(Country.Region, month) %>%
  summarise(Daily_cases = sum(Daily_cases, na.rm = TRUE)) %>% 
  group_by(month) %>% 
  
  slice_max(order_by = Daily_cases, n = 5) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Daily_cases, max)) %>%  
  # filter(Daily_cases > 2000000)

  
  ggplot(aes(x = Daily_cases, y = Country.Region, 
           fill = Country.Region, group = Country.Region )) +
  geom_col() +
  # geom_label(aes(label = Daily_cases), size=2.5, color="white") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  # coord_cartesian(clip = "off") +
  scale_fill_tableau(palette = "Tableau 20") +
  labs(title = "Top 5 Daily cases countries each month",
       subtitle = "Top 5 based on Country's monthly sum of Daily cases in each month",
       caption = "Created by ViSa !!") +
  scale_x_continuous(labels = unit_format(unit = "M", scale = 1e-6))

Adjust ggplotly() size from: https://stackoverflow.com/questions/37241985/resize-plotly-r-ggplotly

ggplotly(grouped_cases_df %>% 
    
  filter(Date > as.Date("2020-02-29")) %>% 
  mutate(month = format(Date, "%Y-%m")) %>%
  
  group_by(Country.Region, month) %>%
  summarise(Daily_cases = sum(Daily_cases, na.rm = TRUE)) %>% 
  group_by(month) %>% 
  
  slice_max(order_by = Daily_cases, n = 5) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Daily_cases, max)) %>%  
  # filter(Daily_cases > 2000000)

  
  ggplot(aes(x = Daily_cases, y = Country.Region, 
           fill = Country.Region, group = Country.Region )) +
  geom_col() +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  # coord_cartesian(clip = "off") +
  scale_fill_tableau(palette = "Tableau 20") +
  labs(title = "Top 5 Daily cases countries each month",
       subtitle = "Top 5 based on Country's monthly sum of Daily cases in each month",
       caption = "Created by ViSa !!") +
  scale_x_continuous(labels = unit_format(unit = "M", scale = 1e-6)),
  
  height = 1200, width=800)

Top Daily cases sum Monthly

grouped_cases_df %>% 
  filter(Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                slice_max(Daily_cases, n=10) %>% 
                                pull(Country.Region)
                              )) %>% 
  
  
  ggplot(aes(x = Date, y = Daily_cases, 
           col = Country.Region )) +
  geom_line(size = 0.8) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~Country.Region) +
  # coord_cartesian(clip = "off") +
  # scale_fill_tableau(palette = "Tableau 20") +
  labs(title = "Daily cases growth of Top 10 countries",
       subtitle = glue("Top 10 countries displayed is from Highest Daily cases as of {max(grouped_cases_df$Date, na.rm = TRUE)}"),
       caption = "Created by ViSa !!") +
  scale_y_continuous(labels = unit_format(unit = "k", scale = 1e-3))

ggplotly(grouped_cases_df %>% 
  filter(Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                slice_max(Daily_cases, n=10) %>% 
                                pull(Country.Region)
                              )) %>% 
  
  
  ggplot(aes(x = Date, y = Daily_cases, 
           col = Country.Region )) +
  geom_line(size = 0.8) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~Country.Region) +
  # coord_cartesian(clip = "off") +
  # scale_fill_tableau(palette = "Tableau 20") +
  labs(title = "Daily cases growth of Top 10 countries",
       subtitle = glue("Top 10 countries displayed is from Highest Daily cases as of {max(grouped_cases_df$Date, na.rm = TRUE)}"),
       caption = "Created by ViSa !!") +
  scale_y_continuous(labels = unit_format(unit = "k", scale = 1e-3))
, height = 800, width = 1000)

Top Daily deaths sum Monthly

grouped_cases_df %>% 
  filter(Country.Region %in% (grouped_cases_df %>% 
                                filter(Date == max(Date, na.rm = TRUE)) %>% 
                                slice_max(Daily_deaths, n=10) %>% 
                                pull(Country.Region)
                              )) %>% 
  
  
  ggplot(aes(x = Date, y = Daily_deaths, 
           col = Country.Region )) +
  geom_line(size = 0.8) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~Country.Region) +
  # coord_cartesian(clip = "off") +
  # scale_fill_tableau(palette = "Tableau 20") +
  labs(title = "Daily deaths growth of Top 10 countries",
       subtitle = glue("Top 10 countries displayed is from Highest Daily deaths as of {max(grouped_cases_df$Date, na.rm = TRUE)}"),
       caption = "Created by ViSa !!") +
  scale_y_continuous(labels = unit_format(unit = "k", scale = 1e-3))

Top Daily deaths sum Monthly

grouped_cases_df %>% 
    
  filter(Date > as.Date("2020-02-29")) %>% 
  mutate(month = format(Date, "%Y-%m")) %>%
  
  group_by(Country.Region, month) %>%
  summarise(Daily_deaths = sum(Daily_deaths, na.rm = TRUE)) %>% 
  group_by(month) %>% 
  
  slice_max(order_by = Daily_deaths, n = 5) %>% 
  ungroup() %>% 
  
  mutate(Country.Region = fct_reorder(Country.Region, Daily_deaths, max)) %>%  
  
  ggplot(aes(x = Daily_deaths, y = Country.Region, 
           fill = Country.Region, group = Country.Region )) +
  geom_col() +
  # geom_label(aes(label = Daily_cases), size=2.5, color="white") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90),
        legend.position = "none") +
  facet_wrap(~month) +
  # coord_cartesian(clip = "off") +
  scale_fill_tableau(palette = "Tableau 20") +
  labs(title = "Top 5 Countries with highest deaths in each month",
       subtitle = "Top 5 based on Country's monthly sum of Daily deaths in each month",
       caption = "Created by ViSa !!") +
  scale_x_continuous(labels = unit_format(unit = "k", scale = 1e-3))

Tooltip Bubble chart

tt_base <- grouped_cases_df %>% 
  arrange(desc(Date)) %>% 
  distinct(Country.Region, .keep_all = TRUE)

tt_base 

tt_inner <- grouped_cases_df %>% 
  select(Country.Region, Date, Daily_cases) %>% 
  mutate(Date = as.integer(Date)) %>% 
  nest(-Country.Region) %>% 
  mutate(
    data = map(data, mutate_mapping, hcaes(x = Date, y = Daily_cases), drop = TRUE),
    data = map(data, list_parse)
  ) %>% 
  rename(tt_nestdata = data)

tt_inner

tt_daily <- left_join(tt_base, tt_inner, by = "Country.Region")

tt_daily

tt_plt1 <- hchart(
  tt_daily,
  "point",
  hcaes(x = Confirmed, y = Daily_cases, name = Country.Region,
        size = Deaths, group = continent, name = Country.Region) 
) %>% 
  
  # hc_yAxis(type = "logarithmic") %>%
  
  hc_tooltip(
  useHTML = TRUE,
  headerFormat = "<b>{point.key}</b>",
  pointFormatter = tooltip_chart(accesor = "tt_nestdata")
  ) %>% 
  
  hc_title(text = "Confirmed Vs Daily Cases as of latest Date") %>% 
  hc_subtitle(text = "Size of bubble based on Deaths <br> (hover over Bubble to view ttchart: Daily cases growth)") %>% 
  hc_caption(text = "created by ViSa!!")

tt_plt1

saveWidget(tt_plt1, "tt_world_cases.html", selfcontained = TRUE, libdir = NULL, background = "white")

saveWidget(tt_plt1, "tt_world_cases2.html", selfcontained = TRUE, libdir = NULL, background = "#FFEADB")

Tooltip Bubble chart2

tt_base <- grouped_cases_df %>% 
  filter(Country.Region != "US") %>% 
  arrange(desc(Date)) %>% 
  distinct(Country.Region, .keep_all = TRUE)

tt_base 

tt_inner <- grouped_cases_df %>% 
  filter(Country.Region != "US") %>% 
  select(Country.Region, Date, Daily_cases) %>% 
  mutate(Date = as.integer(Date)) %>% 
  nest(-Country.Region) %>% 
  mutate(
    data = map(data, mutate_mapping, hcaes(x = Date, y = Daily_cases), drop = TRUE),
    data = map(data, list_parse)
  ) %>% 
  rename(tt_nestdata = data)

tt_inner

tt_daily <- left_join(tt_base, tt_inner, by = "Country.Region")

tt_daily

tt_plt2 <- hchart(
  tt_daily,
  "point",
  hcaes(x = Confirmed, y = Daily_cases, name = Country.Region,
        size = Deaths, group = continent, name = Country.Region) 
) %>% 
  
  # hc_yAxis(type = "logarithmic") %>%
  
  hc_tooltip(
  useHTML = TRUE,
  headerFormat = "<b>{point.key}</b>",
  pointFormatter = tooltip_chart(accesor = "tt_nestdata")
  ) %>% 
  
  hc_title(text = "Confirmed Vs Daily Cases as of latest Date (-US)") %>% 
  hc_subtitle(text = "Size of bubble based on Deaths <br> (hover over Bubble to view ttchart: Daily cases growth)") %>% 
  hc_caption(text = "created by ViSa!!")

tt_plt2

saveWidget(tt_plt2, "tt_world_cases-less_US.html")

saveWidget(tt_plt2, "tt_world_cases-less_US2.html", background = "#FFEADB")

Donut chart